Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We extend lattice decoding to hierarchical phrase-based models, providing a unified treatment with phrase-based decoding by treating lattices as a case of weighted finite-state automata. In the process, we resolve a significant complication that lattice representations introduce in reordering models. Our experiments evaluating the approach demonstrate substantial gains for Chinese-English and Arabic-English translation.